Abstract
This paper introduces a novel retrieval framework for surgery videos. Given a query video, the goal is to retrieve videos in which similar surgical gestures appear. In this framework, the motion content of short video subsequences is modeled, in real-time, using spatiotemporal polynomials. The retrieval engine needs to be trained: key spatiotemporal polynomials, characterizing semantically-relevant surgical gestures, are identified through multiple-instance learning. Then, videos are compared in a high-level space spanned by these key spatiotemporal polynomials. The framework was applied to a dataset of 900 manually-delimited clips from 100 cataract surgery videos. High classification performance (A z = 0.816±0.118) and retrieval performance (MAP = 0.358) were observed.
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Quellec, G., Lamard, M., Droueche, Z., Cochener, B., Roux, C., Cazuguel, G. (2013). A Polynomial Model of Surgical Gestures for Real-Time Retrieval of Surgery Videos. In: Greenspan, H., Müller, H., Syeda-Mahmood, T. (eds) Medical Content-Based Retrieval for Clinical Decision Support. MCBR-CDS 2012. Lecture Notes in Computer Science, vol 7723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36678-9_2
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DOI: https://doi.org/10.1007/978-3-642-36678-9_2
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